
import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

lowerCamelCase__     :		Optional[Any]        = 2



class    _snake_case :


 def __init__(       self       ,	*,  # begin keyword-only arguments
     SCREAMING_SNAKE_CASE_="<s>"       ,	SCREAMING_SNAKE_CASE_="<pad>"       ,	SCREAMING_SNAKE_CASE_="</s>"       ,	SCREAMING_SNAKE_CASE_="<unk>"       ,	SCREAMING_SNAKE_CASE_=None       ,	):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   bos, unk, pad, eos
   lowercase__	: List[str]     		=   []
   lowercase__	: Any     		=   []
   lowercase__	: Dict     		=   {}
   lowercase__	: Tuple     		=   self.add_symbol(__snake_case)
   lowercase__	: Any     		=   self.add_symbol(__snake_case)
   lowercase__	: Optional[Any]     		=   self.add_symbol(__snake_case)
   lowercase__	: Dict     		=   self.add_symbol(__snake_case)
   if extra_special_symbols:
     for s in extra_special_symbols:
       self.add_symbol(__snake_case)
   lowercase__	: Tuple     		=   len(self.symbols)


 def __eq__(       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return self.indices == other.indices


 def __getitem__(       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if idx < len(self.symbols):
     return self.symbols[idx]
   return self.unk_word


 def __len__(       self):


   '''simple docstring'''




   return len(self.symbols)


 def __contains__(       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return sym in self.indices


 @classmethod
 def   lowercase__  (       cls       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Dict     		=   cls()
   d.add_from_file(__snake_case)
   return d


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=1       ,	SCREAMING_SNAKE_CASE_=False):


   '''simple docstring'''




   if word in self.indices and not overwrite:
     lowercase__	: Union[str, Any]     		=   self.indices[word]
     lowercase__	: Tuple     		=   self.count[idx] + n
     return idx
   else:
     lowercase__	: Optional[Any]     		=   len(self.symbols)
     lowercase__	: List[Any]     		=   idx
     self.symbols.append(__snake_case)
     self.count.append(__snake_case)
     return idx


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return 0



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if isinstance(__snake_case       ,	__snake_case):
     try:
       with open(__snake_case       ,	"""r"""       ,	encoding="""utf-8""") as fd:
         self.add_from_file(__snake_case)
     except FileNotFoundError as fnfe:
       raise fnfe
     except UnicodeError:
       raise Exception("""Incorrect encoding detected in {}, please rebuild the dataset""".format(__snake_case))
     return

   lowercase__	: List[Any]     		=   f.readlines()
   lowercase__	: Optional[Any]     		=   self._load_meta(__snake_case)

   for line in lines[indices_start_line:]:
     try:
       lowercase__	: Optional[int]     		=   line.rstrip().rsplit(""" """       ,	1)
       if field == "#fairseq:overwrite":
         lowercase__	: List[str]     		=   True
         lowercase__	: Tuple     		=   line.rsplit(""" """       ,	1)
       else:
         lowercase__	: Tuple     		=   False
       lowercase__	: int     		=   int(__snake_case)
       lowercase__	: Optional[Any]     		=   line
       if word in self and not overwrite:
         raise RuntimeError(
             """Duplicate word found when loading Dictionary: '{}'. """
             """Duplicate words can overwrite earlier ones by adding the """
             """#fairseq:overwrite flag at the end of the corresponding row """
             """in the dictionary file. If using the Camembert model, please """
             """download an updated copy of the model file.""".format(__snake_case))
       self.add_symbol(__snake_case       ,	n=__snake_case       ,	overwrite=__snake_case)
     except ValueError:
       raise ValueError("""Incorrect dictionary format, expected '<token> <cnt> [flags]'""")

def 		UpperCamelCase						(   lowercase_ )		->						int:


  '''simple docstring'''






  lowercase__	: Union[str, Any]     		=   dict((re.sub(R"""@@$"""			,			""""""			,			SCREAMING_SNAKE_CASE__ ), v) if k.endswith("""@@""" ) else (re.sub(R"""$"""			,			"""</w>"""			,			SCREAMING_SNAKE_CASE__ ), v) for k, v in d.items() )
  lowercase__	: str     		=   """<s> <pad> </s> <unk>""".split()
  # restore the special tokens
  for k in keep_keys:
    del da[F'{k}</w>']
    lowercase__	: List[Any]     		=   d[k]  # restore
  return da

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						Optional[int]:


  '''simple docstring'''






  if not os.path.exists(SCREAMING_SNAKE_CASE__ ):
    raise ValueError(F'path {biogpt_checkpoint_path} does not exist!' )
  os.makedirs(SCREAMING_SNAKE_CASE__			,			exist_ok=SCREAMING_SNAKE_CASE__ )
  print(F'Writing results to {pytorch_dump_folder_path}' )

  # handle various types of models

  lowercase__	: Tuple     		=   os.path.join(SCREAMING_SNAKE_CASE__			,			"""checkpoint.pt""" )
  if not os.path.isfile(SCREAMING_SNAKE_CASE__ ):
    raise ValueError(F'path to the file {checkpoint_file} does not exist!' )
  lowercase__	: str     		=   torch.load(SCREAMING_SNAKE_CASE__			,			map_location="""cpu""" )

  lowercase__	: List[Any]     		=   chkpt["""cfg"""]["""model"""]

  # dicts
  lowercase__	: Dict     		=   os.path.join(SCREAMING_SNAKE_CASE__			,			"""dict.txt""" )
  if not os.path.isfile(SCREAMING_SNAKE_CASE__ ):
    raise ValueError(F'path to the file {dict_file} does not exist!' )
  lowercase__	: Optional[int]     		=   Dictionary.load(SCREAMING_SNAKE_CASE__ )
  lowercase__	: Optional[Any]     		=   rewrite_dict_keys(src_dict.indices )
  lowercase__	: List[Any]     		=   len(SCREAMING_SNAKE_CASE__ )
  lowercase__	: Tuple     		=   os.path.join(SCREAMING_SNAKE_CASE__			,			VOCAB_FILES_NAMES["""vocab_file"""] )
  print(F'Generating {src_vocab_file} of {src_vocab_size} records' )
  with open(SCREAMING_SNAKE_CASE__			,			"""w"""			,			encoding="""utf-8""" ) as f:
    f.write(json.dumps(SCREAMING_SNAKE_CASE__			,			ensure_ascii=SCREAMING_SNAKE_CASE__			,			indent=SCREAMING_SNAKE_CASE__ ) )

  # merges_file (bpecodes)
  lowercase__	: int     		=   os.path.join(SCREAMING_SNAKE_CASE__			,			"""bpecodes""" )
  if not os.path.isfile(SCREAMING_SNAKE_CASE__ ):
    raise ValueError(F'path to the file {bpecodes_file} does not exist!' )

  lowercase__	: Optional[Any]     		=   os.path.join(SCREAMING_SNAKE_CASE__			,			VOCAB_FILES_NAMES["""merges_file"""] )
  shutil.copyfile(SCREAMING_SNAKE_CASE__			,			SCREAMING_SNAKE_CASE__ )

  # model config
  lowercase__	: int     		=   os.path.join(SCREAMING_SNAKE_CASE__			,			"""config.json""" )

  lowercase__	: Union[str, Any]     		=   {
      """activation_dropout""": args["""activation_dropout"""],
      """architectures""": ["""BioGptForCausalLM"""],
      """attention_probs_dropout_prob""": args["""attention_dropout"""],
      """bos_token_id""": 0,
      """eos_token_id""": 2,
      """hidden_act""": args["""activation_fn"""],
      """hidden_dropout_prob""": args["""dropout"""],
      """hidden_size""": args["""decoder_embed_dim"""],
      """initializer_range""": 0.02,
      """intermediate_size""": args["""decoder_ffn_embed_dim"""],
      """layer_norm_eps""": 1E-12,
      """layerdrop""": args["""decoder_layerdrop"""],
      """max_position_embeddings""": args["""max_target_positions"""],
      """model_type""": """biogpt""",
      """num_attention_heads""": args["""decoder_attention_heads"""],
      """num_hidden_layers""": args["""decoder_layers"""],
      """pad_token_id""": 1,
      """scale_embedding""": not args["""no_scale_embedding"""],
      """tie_word_embeddings""": args["""share_decoder_input_output_embed"""],
      """vocab_size""": src_vocab_size,
  }

  # good hparam defaults to start with

  print(F'Generating {biogpt_model_config_file}' )
  with open(SCREAMING_SNAKE_CASE__			,			"""w"""			,			encoding="""utf-8""" ) as f:
    f.write(json.dumps(SCREAMING_SNAKE_CASE__			,			ensure_ascii=SCREAMING_SNAKE_CASE__			,			indent=SCREAMING_SNAKE_CASE__ ) )

  # tokenizer config
  lowercase__	: Any     		=   os.path.join(SCREAMING_SNAKE_CASE__			,			SCREAMING_SNAKE_CASE__ )

  lowercase__	: str     		=   {
      """bos_token""": """<s>""",
      """eos_token""": """</s>""",
      """model_max_length""": 10_24,
      """pad_token""": """<pad>""",
      """special_tokens_map_file""": None,
      """tokenizer_class""": """BioGptTokenizer""",
      """unk_token""": """<unk>""",
  }

  print(F'Generating {biogpt_tokenizer_config_file}' )
  with open(SCREAMING_SNAKE_CASE__			,			"""w"""			,			encoding="""utf-8""" ) as f:
    f.write(json.dumps(SCREAMING_SNAKE_CASE__			,			ensure_ascii=SCREAMING_SNAKE_CASE__			,			indent=SCREAMING_SNAKE_CASE__ ) )

  # model
  lowercase__	: Any     		=   chkpt["""model"""]

  # remove unneeded keys
  lowercase__	: List[Any]     		=   [
      """decoder.version""",
  ]
  for k in ignore_keys:
    model_state_dict.pop(SCREAMING_SNAKE_CASE__			,			SCREAMING_SNAKE_CASE__ )

  lowercase__	: List[Any]     		=   list(model_state_dict.keys() )
  for layer_name in layer_names:
    if layer_name.endswith("""output_projection.weight""" ):
      lowercase__	: Optional[Any]     		=   model_state_dict.pop(SCREAMING_SNAKE_CASE__ )
    else:
      lowercase__	: Dict     		=   model_state_dict.pop(SCREAMING_SNAKE_CASE__ )

  lowercase__	: List[str]     		=   BioGptConfig.from_pretrained(SCREAMING_SNAKE_CASE__ )
  lowercase__	: Tuple     		=   BioGptForCausalLM(SCREAMING_SNAKE_CASE__ )

  # check that it loads ok
  model_new.load_state_dict(SCREAMING_SNAKE_CASE__ )

  # save
  lowercase__	: Union[str, Any]     		=   os.path.join(SCREAMING_SNAKE_CASE__			,			SCREAMING_SNAKE_CASE__ )
  print(F'Generating {pytorch_weights_dump_path}' )
  torch.save(SCREAMING_SNAKE_CASE__			,			SCREAMING_SNAKE_CASE__ )

  print("""Conversion is done!""" )


if __name__ == "__main__":
     lowerCamelCase__     :		Union[str, Any]        = argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--biogpt_checkpoint_path""",
         default=None,
         type=str,
         required=True,
         help=(
             """Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"""
             """ bpecodes, etc."""
         ),
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     lowerCamelCase__     :		Any        = parser.parse_args()
     convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)










'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class        lowercase__    (		_snake_case	,	unittest.TestCase   ):





							'''simple docstring'''


							A_				: Dict       =    KandinskyVaaPipeline
							A_				: List[str]       =    [
							    """image_embeds""",
							    """negative_image_embeds""",
							]
							A_				: Optional[int]       =    ["""image_embeds""", """negative_image_embeds"""]
							A_				: Optional[Any]       =    [
							    """generator""",
							    """height""",
							    """width""",
							    """latents""",
							    """guidance_scale""",
							    """num_inference_steps""",
							    """return_dict""",
							    """guidance_scale""",
							    """num_images_per_prompt""",
							    """output_type""",
							    """return_dict""",
							]
							A_				: Dict       =    False






							@property
							def UpperCAmelCase_      (       self    ):
											return 32






							@property
							def UpperCAmelCase_      (       self    ):
											return 32






							@property
							def UpperCAmelCase_      (       self    ):
											return self.time_input_dim






							@property
							def UpperCAmelCase_      (       self    ):
											return self.time_input_dim * 4






							@property
							def UpperCAmelCase_      (       self    ):
											return 100






							@property
							def UpperCAmelCase_      (       self    ):
											torch.manual_seed(0    )

											_SCREAMING_SNAKE_CASE					:			Tuple							= {
											    """in_channels""": 4,
											    # Out channels is double in channels because predicts mean and variance
											    """out_channels""": 8,
											    """addition_embed_type""": """image""",
											    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
											    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
											    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
											    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
											    """layers_per_block""": 1,
											    """encoder_hid_dim""": self.text_embedder_hidden_size,
											    """encoder_hid_dim_type""": """image_proj""",
											    """cross_attention_dim""": self.cross_attention_dim,
											    """attention_head_dim""": 4,
											    """resnet_time_scale_shift""": """scale_shift""",
											    """class_embed_type""": None,
											}

											_SCREAMING_SNAKE_CASE					:			Any							= UNetaDConditionModel(**__snake_case    )
											return model






							@property
							def UpperCAmelCase_      (       self    ):
											return {
											    "block_out_channels": [32, 64],
											    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
											    "in_channels": 3,
											    "latent_channels": 4,
											    "layers_per_block": 1,
											    "norm_num_groups": 8,
											    "norm_type": "spatial",
											    "num_vq_embeddings": 12,
											    "out_channels": 3,
											    "up_block_types": [
											        "AttnUpDecoderBlock2D",
											        "UpDecoderBlock2D",
											    ],
											    "vq_embed_dim": 4,
											}






							@property
							def UpperCAmelCase_      (       self    ):
											torch.manual_seed(0    )
											_SCREAMING_SNAKE_CASE					:			Tuple							= VQModel(**self.dummy_movq_kwargs    )
											return model






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			int							= self.dummy_unet
											_SCREAMING_SNAKE_CASE					:			List[str]							= self.dummy_movq

											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= DDIMScheduler(
											    num_train_timesteps=1000       ,   beta_schedule="""linear"""       ,   beta_start=0.00085       ,   beta_end=0.012       ,   clip_sample=__snake_case       ,   set_alpha_to_one=__snake_case       ,   steps_offset=1       ,   prediction_type="""epsilon"""       ,   thresholding=__snake_case       ,   )

											_SCREAMING_SNAKE_CASE					:			Dict							= {
											    """unet""": unet,
											    """scheduler""": scheduler,
											    """movq""": movq,
											}
											return components






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case=0    ):
											_SCREAMING_SNAKE_CASE					:			int							= floats_tensor((1, self.text_embedder_hidden_size)       ,   rng=random.Random(__snake_case    )    ).to(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			List[str]							= floats_tensor((1, self.text_embedder_hidden_size)       ,   rng=random.Random(seed + 1    )    ).to(
											    __snake_case    )
											if str(__snake_case    ).startswith("""mps"""    ):
															_SCREAMING_SNAKE_CASE					:			str							= torch.manual_seed(__snake_case    )
											else:
															_SCREAMING_SNAKE_CASE					:			Dict							= torch.Generator(device=__snake_case    ).manual_seed(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Tuple							= {
											    """image_embeds""": image_embeds,
											    """negative_image_embeds""": negative_image_embeds,
											    """generator""": generator,
											    """height""": 64,
											    """width""": 64,
											    """guidance_scale""": 4.0,
											    """num_inference_steps""": 2,
											    """output_type""": """np""",
											}
											return inputs






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= """cpu"""

											_SCREAMING_SNAKE_CASE					:			Dict							= self.get_dummy_components()

											_SCREAMING_SNAKE_CASE					:			List[Any]							= self.pipeline_class(**__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= pipe.to(__snake_case    )

											pipe.set_progress_bar_config(disable=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Any							= pipe(**self.get_dummy_inputs(__snake_case    )    )
											_SCREAMING_SNAKE_CASE					:			Tuple							= output.images

											_SCREAMING_SNAKE_CASE					:			Dict							= pipe(
											    **self.get_dummy_inputs(__snake_case    )       ,   return_dict=__snake_case       ,   )[0]

											_SCREAMING_SNAKE_CASE					:			Tuple							= image[0, -3:, -3:, -1]
											_SCREAMING_SNAKE_CASE					:			List[Any]							= image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 64, 64, 3)

											_SCREAMING_SNAKE_CASE					:			Dict							= np.array(
											    [0.6237976, 1.0, 0.36441332, 1.0, 0.70639634, 0.29877186, 0.85652125, 0.5216843, 0.54454046]    )

											assert (
											    np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
											), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""

											assert (
											    np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1e-2
											), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""





@slow
@require_torch_gpu
class        lowercase__    (		unittest.TestCase   ):





							'''simple docstring'''


							def UpperCAmelCase_      (       self    ):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			List[str]							= load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"""    )

											_SCREAMING_SNAKE_CASE					:			List[Any]							= KandinskyVaaPriorPipeline.from_pretrained(
											    """kandinsky-community/kandinsky-2-2-prior"""       ,   torch_dtype=torch.floataa    )
											pipe_prior.to(__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Tuple							= KandinskyVaaPipeline.from_pretrained(
											    """kandinsky-community/kandinsky-2-2-decoder"""       ,   torch_dtype=torch.floataa    )
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= pipeline.to(__snake_case    )
											pipeline.set_progress_bar_config(disable=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Dict							= """red cat, 4k photo"""

											_SCREAMING_SNAKE_CASE					:			List[str]							= torch.Generator(device="""cuda"""    ).manual_seed(0    )
											_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[str]							= pipe_prior(
											    __snake_case       ,   generator=__snake_case       ,   num_inference_steps=5       ,   negative_prompt=""""""       ,   ).to_tuple()

											_SCREAMING_SNAKE_CASE					:			Dict							= torch.Generator(device="""cuda"""    ).manual_seed(0    )
											_SCREAMING_SNAKE_CASE					:			List[str]							= pipeline(
											    image_embeds=__snake_case       ,   negative_image_embeds=__snake_case       ,   generator=__snake_case       ,   num_inference_steps=100       ,   output_type="""np"""       ,   )

											_SCREAMING_SNAKE_CASE					:			List[Any]							= output.images[0]

											assert image.shape == (512, 512, 3)

											assert_mean_pixel_difference(__snake_case       ,   __snake_case    )




